Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations1068
Missing cells3380
Missing cells (%)12.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory208.7 KiB
Average record size in memory200.1 B

Variable types

DateTime1
Text2
Categorical4
Numeric17
Unsupported1

Alerts

nat has constant value "AC" Constant
em has constant value "LML" Constant
al2o3 is highly overall correlated with caot and 4 other fieldsHigh correlation
caot is highly overall correlated with al2o3 and 4 other fieldsHigh correlation
g45µ is highly overall correlated with g75µHigh correlation
g75µ is highly overall correlated with g45µHigh correlation
k2o is highly overall correlated with al2o3 and 2 other fieldsHigh correlation
mgo is highly overall correlated with na2oHigh correlation
ml is highly overall correlated with pl and 1 other fieldsHigh correlation
na2o is highly overall correlated with al2o3 and 4 other fieldsHigh correlation
pf is highly overall correlated with al2o3 and 3 other fieldsHigh correlation
pl is highly overall correlated with ml and 1 other fieldsHigh correlation
r1_iram1622 is highly overall correlated with r2_iram1622 and 1 other fieldsHigh correlation
r28_iram1622 is highly overall correlated with r2_iram1622 and 1 other fieldsHigh correlation
r2_iram1622 is highly overall correlated with r1_iram1622 and 3 other fieldsHigh correlation
r3_iram1622 is highly overall correlated with ml and 4 other fieldsHigh correlation
r7_iram1622 is highly overall correlated with r28_iram1622 and 2 other fieldsHigh correlation
sio2 is highly overall correlated with al2o3 and 4 other fieldsHigh correlation
g75µ has 30 (2.8%) missing values Missing
g45µ has 24 (2.2%) missing values Missing
sba has 26 (2.4%) missing values Missing
r1_iram1622 has 662 (62.0%) missing values Missing
r2_iram1622 has 105 (9.8%) missing values Missing
r3_iram1622 has 930 (87.1%) missing values Missing
r7_iram1622 has 272 (25.5%) missing values Missing
r28_iram1622 has 57 (5.3%) missing values Missing
r91_iram1622 has 1068 (100.0%) missing values Missing
pf has 26 (2.4%) missing values Missing
so3 has 22 (2.1%) missing values Missing
mgo has 23 (2.2%) missing values Missing
sio2 has 22 (2.1%) missing values Missing
fe2o3 has 22 (2.1%) missing values Missing
caot has 22 (2.1%) missing values Missing
al2o3 has 22 (2.1%) missing values Missing
na2o has 25 (2.3%) missing values Missing
k2o has 22 (2.1%) missing values Missing
r91_iram1622 is an unsupported type, check if it needs cleaning or further analysis Unsupported
g75µ has 13 (1.2%) zeros Zeros

Reproduction

Analysis started2024-11-08 19:36:25.387249
Analysis finished2024-11-08 19:37:43.668460
Duration1 minute and 18.28 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

date
Date

Distinct683
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
Minimum2018-01-02 00:00:00
Maximum2021-04-13 00:00:00
2024-11-08T14:37:43.913124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:44.296020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ric
Text

Distinct892
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
2024-11-08T14:37:45.604188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6408
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique754 ?
Unique (%)70.6%

Sample

1st row7707ST
2nd row7702ST
3rd row7722ST
4th row7726ST
5th row7729ST
ValueCountFrequency (%)
7722st 4
 
0.4%
7726st 4
 
0.4%
7727st 4
 
0.4%
7779st 4
 
0.4%
8272st 4
 
0.4%
8727st 4
 
0.4%
2727st 3
 
0.3%
5677st 3
 
0.3%
6877st 3
 
0.3%
2772st 3
 
0.3%
Other values (882) 1032
96.6%
2024-11-08T14:37:47.028993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 1068
16.7%
S 1068
16.7%
7 964
15.0%
2 775
12.1%
8 511
8.0%
6 474
7.4%
5 467
7.3%
9 457
7.1%
0 320
 
5.0%
1 304
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6408
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1068
16.7%
S 1068
16.7%
7 964
15.0%
2 775
12.1%
8 511
8.0%
6 474
7.4%
5 467
7.3%
9 457
7.1%
0 320
 
5.0%
1 304
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6408
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1068
16.7%
S 1068
16.7%
7 964
15.0%
2 775
12.1%
8 511
8.0%
6 474
7.4%
5 467
7.3%
9 457
7.1%
0 320
 
5.0%
1 304
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6408
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1068
16.7%
S 1068
16.7%
7 964
15.0%
2 775
12.1%
8 511
8.0%
6 474
7.4%
5 467
7.3%
9 457
7.1%
0 320
 
5.0%
1 304
 
4.7%

nat
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
AC
1068 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2136
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAC
2nd rowAC
3rd rowAC
4th rowAC
5th rowAC

Common Values

ValueCountFrequency (%)
AC 1068
100.0%

Length

2024-11-08T14:37:47.395870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T14:37:47.705334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ac 1068
100.0%

Most occurring characters

ValueCountFrequency (%)
A 1068
50.0%
C 1068
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2136
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1068
50.0%
C 1068
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2136
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1068
50.0%
C 1068
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2136
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1068
50.0%
C 1068
50.0%

pl
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
2
571 
3
497 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1068
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row3
5th row2

Common Values

ValueCountFrequency (%)
2 571
53.5%
3 497
46.5%

Length

2024-11-08T14:37:47.970727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T14:37:48.228973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 571
53.5%
3 497
46.5%

Most occurring characters

ValueCountFrequency (%)
2 571
53.5%
3 497
46.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1068
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 571
53.5%
3 497
46.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1068
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 571
53.5%
3 497
46.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1068
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 571
53.5%
3 497
46.5%

em
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
LML
1068 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3204
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLML
2nd rowLML
3rd rowLML
4th rowLML
5th rowLML

Common Values

ValueCountFrequency (%)
LML 1068
100.0%

Length

2024-11-08T14:37:48.450665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T14:37:48.601991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
lml 1068
100.0%

Most occurring characters

ValueCountFrequency (%)
L 2136
66.7%
M 1068
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 2136
66.7%
M 1068
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 2136
66.7%
M 1068
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 2136
66.7%
M 1068
33.3%

ml
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
2
571 
3
497 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1068
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row3
5th row2

Common Values

ValueCountFrequency (%)
2 571
53.5%
3 497
46.5%

Length

2024-11-08T14:37:48.838317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T14:37:49.054256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 571
53.5%
3 497
46.5%

Most occurring characters

ValueCountFrequency (%)
2 571
53.5%
3 497
46.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1068
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 571
53.5%
3 497
46.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1068
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 571
53.5%
3 497
46.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1068
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 571
53.5%
3 497
46.5%

rie
Text

Distinct892
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
2024-11-08T14:37:50.380863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6408
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique754 ?
Unique (%)70.6%

Sample

1st row7707ST
2nd row7702ST
3rd row7722ST
4th row7726ST
5th row7729ST
ValueCountFrequency (%)
7722st 4
 
0.4%
7726st 4
 
0.4%
7727st 4
 
0.4%
7779st 4
 
0.4%
8272st 4
 
0.4%
8727st 4
 
0.4%
2727st 3
 
0.3%
5677st 3
 
0.3%
6877st 3
 
0.3%
2772st 3
 
0.3%
Other values (882) 1032
96.6%
2024-11-08T14:37:52.054185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 1068
16.7%
S 1068
16.7%
7 964
15.0%
2 775
12.1%
8 511
8.0%
6 474
7.4%
5 467
7.3%
9 457
7.1%
0 320
 
5.0%
1 304
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6408
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1068
16.7%
S 1068
16.7%
7 964
15.0%
2 775
12.1%
8 511
8.0%
6 474
7.4%
5 467
7.3%
9 457
7.1%
0 320
 
5.0%
1 304
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6408
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1068
16.7%
S 1068
16.7%
7 964
15.0%
2 775
12.1%
8 511
8.0%
6 474
7.4%
5 467
7.3%
9 457
7.1%
0 320
 
5.0%
1 304
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6408
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1068
16.7%
S 1068
16.7%
7 964
15.0%
2 775
12.1%
8 511
8.0%
6 474
7.4%
5 467
7.3%
9 457
7.1%
0 320
 
5.0%
1 304
 
4.7%

g75µ
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct80
Distinct (%)7.7%
Missing30
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean0.25845857
Minimum0
Maximum3.74
Zeros13
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-11-08T14:37:52.478993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.08
Q10.17
median0.23
Q30.3
95-th percentile0.47
Maximum3.74
Range3.74
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.22480107
Coefficient of variation (CV)0.86977601
Kurtosis102.90931
Mean0.25845857
Median Absolute Deviation (MAD)0.07
Skewness8.4338194
Sum268.28
Variance0.050535519
MonotonicityNot monotonic
2024-11-08T14:37:52.861558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.19 68
 
6.4%
0.26 61
 
5.7%
0.22 53
 
5.0%
0.24 43
 
4.0%
0.2 42
 
3.9%
0.29 41
 
3.8%
0.16 41
 
3.8%
0.3 37
 
3.5%
0.23 36
 
3.4%
0.15 35
 
3.3%
Other values (70) 581
54.4%
ValueCountFrequency (%)
0 13
1.2%
0.01 1
 
0.1%
0.02 3
 
0.3%
0.03 1
 
0.1%
0.05 2
 
0.2%
0.06 10
0.9%
0.07 17
1.6%
0.08 14
1.3%
0.09 24
2.2%
0.1 14
1.3%
ValueCountFrequency (%)
3.74 1
0.1%
3.12 1
0.1%
2.6 1
0.1%
2.24 1
0.1%
2.1 1
0.1%
1.71 1
0.1%
1.39 1
0.1%
1.18 1
0.1%
0.85 1
0.1%
0.81 1
0.1%

g45µ
Real number (ℝ)

High correlation  Missing 

Distinct219
Distinct (%)21.0%
Missing24
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean2.2100383
Minimum0.06
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-11-08T14:37:53.280576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.06
5-th percentile1.58
Q11.88
median2.15
Q32.44
95-th percentile3.1485
Maximum6
Range5.94
Interquartile range (IQR)0.56

Descriptive statistics

Standard deviation0.52697949
Coefficient of variation (CV)0.23844812
Kurtosis6.0466105
Mean2.2100383
Median Absolute Deviation (MAD)0.28
Skewness1.3459524
Sum2307.28
Variance0.27770738
MonotonicityNot monotonic
2024-11-08T14:37:53.538223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 33
 
3.1%
1.9 32
 
3.0%
2.1 31
 
2.9%
2.4 25
 
2.3%
2.2 18
 
1.7%
1.8 18
 
1.7%
2.3 17
 
1.6%
2.27 16
 
1.5%
2.6 15
 
1.4%
1.64 15
 
1.4%
Other values (209) 824
77.2%
(Missing) 24
 
2.2%
ValueCountFrequency (%)
0.06 1
0.1%
0.26 1
0.1%
0.41 1
0.1%
0.55 1
0.1%
1 1
0.1%
1.06 1
0.1%
1.14 1
0.1%
1.15 1
0.1%
1.28 1
0.1%
1.29 1
0.1%
ValueCountFrequency (%)
6 1
0.1%
5.5 1
0.1%
4.75 1
0.1%
4.59 1
0.1%
4.49 1
0.1%
4.45 2
0.2%
4.22 1
0.1%
4.02 1
0.1%
3.77 1
0.1%
3.76 1
0.1%

sba
Real number (ℝ)

Missing 

Distinct115
Distinct (%)11.0%
Missing26
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean418.78407
Minimum330
Maximum502
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-11-08T14:37:53.829099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum330
5-th percentile389
Q1407
median418
Q3430
95-th percentile452
Maximum502
Range172
Interquartile range (IQR)23

Descriptive statistics

Standard deviation19.932075
Coefficient of variation (CV)0.047595113
Kurtosis1.313931
Mean418.78407
Median Absolute Deviation (MAD)12
Skewness0.32773235
Sum436373
Variance397.28762
MonotonicityNot monotonic
2024-11-08T14:37:54.178837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
411 28
 
2.6%
421 28
 
2.6%
426 27
 
2.5%
420 25
 
2.3%
428 25
 
2.3%
416 25
 
2.3%
410 24
 
2.2%
418 24
 
2.2%
417 24
 
2.2%
419 24
 
2.2%
Other values (105) 788
73.8%
(Missing) 26
 
2.4%
ValueCountFrequency (%)
330 1
0.1%
350 1
0.1%
363 1
0.1%
365 1
0.1%
366 1
0.1%
370 1
0.1%
371 1
0.1%
372 2
0.2%
374 2
0.2%
376 2
0.2%
ValueCountFrequency (%)
502 1
0.1%
495 1
0.1%
494 1
0.1%
489 1
0.1%
487 1
0.1%
484 2
0.2%
479 1
0.1%
477 2
0.2%
476 1
0.1%
475 1
0.1%

r1_iram1622
Real number (ℝ)

High correlation  Missing 

Distinct92
Distinct (%)22.7%
Missing662
Missing (%)62.0%
Infinite0
Infinite (%)0.0%
Mean18.278079
Minimum12.1
Maximum32.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-11-08T14:37:54.511410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum12.1
5-th percentile14.6
Q116.9
median18.25
Q319.8
95-th percentile21.6
Maximum32.5
Range20.4
Interquartile range (IQR)2.9

Descriptive statistics

Standard deviation2.2780623
Coefficient of variation (CV)0.12463357
Kurtosis4.7269763
Mean18.278079
Median Absolute Deviation (MAD)1.45
Skewness0.75617339
Sum7420.9
Variance5.1895677
MonotonicityNot monotonic
2024-11-08T14:37:54.846069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.4 13
 
1.2%
17.4 13
 
1.2%
18.9 11
 
1.0%
18.1 11
 
1.0%
16.9 10
 
0.9%
19.8 9
 
0.8%
19 9
 
0.8%
17.1 9
 
0.8%
16 8
 
0.7%
17.3 8
 
0.7%
Other values (82) 305
28.6%
(Missing) 662
62.0%
ValueCountFrequency (%)
12.1 2
0.2%
12.6 1
 
0.1%
13.2 2
0.2%
13.4 1
 
0.1%
13.7 3
0.3%
13.9 2
0.2%
14.1 1
 
0.1%
14.3 2
0.2%
14.5 4
0.4%
14.6 4
0.4%
ValueCountFrequency (%)
32.5 1
 
0.1%
30.3 1
 
0.1%
23.6 1
 
0.1%
23.1 1
 
0.1%
22.8 1
 
0.1%
22.6 2
0.2%
22.3 3
0.3%
22.2 1
 
0.1%
22.1 2
0.2%
22 4
0.4%

r2_iram1622
Real number (ℝ)

High correlation  Missing 

Distinct103
Distinct (%)10.7%
Missing105
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean31.131568
Minimum23.9
Maximum36.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-11-08T14:37:55.190315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum23.9
5-th percentile27.9
Q129.8
median31.2
Q332.5
95-th percentile34.4
Maximum36.8
Range12.9
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation2.0396538
Coefficient of variation (CV)0.065517219
Kurtosis0.12359534
Mean31.131568
Median Absolute Deviation (MAD)1.4
Skewness-0.28553116
Sum29979.7
Variance4.1601875
MonotonicityNot monotonic
2024-11-08T14:37:55.537542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.9 26
 
2.4%
32.1 25
 
2.3%
30.4 25
 
2.3%
30.5 21
 
2.0%
30.7 21
 
2.0%
31.3 21
 
2.0%
31.2 21
 
2.0%
30.9 21
 
2.0%
31.6 20
 
1.9%
30.6 20
 
1.9%
Other values (93) 742
69.5%
(Missing) 105
 
9.8%
ValueCountFrequency (%)
23.9 1
 
0.1%
24.7 1
 
0.1%
24.8 2
0.2%
24.9 1
 
0.1%
25.1 1
 
0.1%
25.4 1
 
0.1%
25.6 1
 
0.1%
25.8 3
0.3%
25.9 1
 
0.1%
26.1 2
0.2%
ValueCountFrequency (%)
36.8 1
 
0.1%
36.4 1
 
0.1%
36.3 2
 
0.2%
35.9 1
 
0.1%
35.7 2
 
0.2%
35.2 2
 
0.2%
35.1 5
0.5%
35 4
0.4%
34.9 6
0.6%
34.8 2
 
0.2%

r3_iram1622
Real number (ℝ)

High correlation  Missing 

Distinct78
Distinct (%)56.5%
Missing930
Missing (%)87.1%
Infinite0
Infinite (%)0.0%
Mean37.934058
Minimum30.1
Maximum48.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-11-08T14:37:55.888818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum30.1
5-th percentile33.085
Q135.925
median38
Q339.675
95-th percentile41.83
Maximum48.4
Range18.3
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation2.8120309
Coefficient of variation (CV)0.074129451
Kurtosis1.0618832
Mean37.934058
Median Absolute Deviation (MAD)1.85
Skewness0.075963943
Sum5234.9
Variance7.9075177
MonotonicityNot monotonic
2024-11-08T14:37:56.428920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.6 5
 
0.5%
34.8 4
 
0.4%
39.1 4
 
0.4%
36.9 4
 
0.4%
38 4
 
0.4%
35 3
 
0.3%
38.4 3
 
0.3%
37.8 3
 
0.3%
38.2 3
 
0.3%
39.8 3
 
0.3%
Other values (68) 102
 
9.6%
(Missing) 930
87.1%
ValueCountFrequency (%)
30.1 1
0.1%
30.9 1
0.1%
31.8 2
0.2%
31.9 1
0.1%
32.9 1
0.1%
33 1
0.1%
33.1 1
0.1%
33.7 1
0.1%
34.1 2
0.2%
34.3 1
0.1%
ValueCountFrequency (%)
48.4 1
0.1%
44.3 1
0.1%
44.2 1
0.1%
43.3 1
0.1%
43.2 1
0.1%
42.2 1
0.1%
42 1
0.1%
41.8 1
0.1%
41.7 1
0.1%
41.6 2
0.2%

r7_iram1622
Real number (ℝ)

High correlation  Missing 

Distinct120
Distinct (%)15.1%
Missing272
Missing (%)25.5%
Infinite0
Infinite (%)0.0%
Mean47.457412
Minimum40
Maximum53.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-11-08T14:37:56.754508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile43.2
Q145.8
median47.5
Q349.2
95-th percentile51.4
Maximum53.9
Range13.9
Interquartile range (IQR)3.4

Descriptive statistics

Standard deviation2.4610286
Coefficient of variation (CV)0.051857624
Kurtosis-0.14404003
Mean47.457412
Median Absolute Deviation (MAD)1.7
Skewness-0.14717313
Sum37776.1
Variance6.056662
MonotonicityNot monotonic
2024-11-08T14:37:57.078171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.2 21
 
2.0%
47.9 18
 
1.7%
46.5 17
 
1.6%
46.8 17
 
1.6%
45.9 16
 
1.5%
47.3 15
 
1.4%
48.6 15
 
1.4%
47.1 15
 
1.4%
48.4 14
 
1.3%
48.1 14
 
1.3%
Other values (110) 634
59.4%
(Missing) 272
25.5%
ValueCountFrequency (%)
40 1
0.1%
40.3 2
0.2%
40.7 1
0.1%
40.8 1
0.1%
41 2
0.2%
41.3 1
0.1%
41.7 1
0.1%
41.8 1
0.1%
42 1
0.1%
42.1 1
0.1%
ValueCountFrequency (%)
53.9 1
 
0.1%
53.7 1
 
0.1%
53.4 1
 
0.1%
53.3 1
 
0.1%
53.2 1
 
0.1%
53.1 1
 
0.1%
53 1
 
0.1%
52.7 4
0.4%
52.6 2
0.2%
52.5 3
0.3%

r28_iram1622
Real number (ℝ)

High correlation  Missing 

Distinct105
Distinct (%)10.4%
Missing57
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean58.320277
Minimum52.2
Maximum64.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-11-08T14:37:57.397826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum52.2
5-th percentile54.8
Q157
median58.4
Q359.8
95-th percentile61.45
Maximum64.3
Range12.1
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.0379067
Coefficient of variation (CV)0.034943364
Kurtosis-0.22074127
Mean58.320277
Median Absolute Deviation (MAD)1.4
Skewness-0.17125425
Sum58961.8
Variance4.1530637
MonotonicityNot monotonic
2024-11-08T14:37:57.729047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.3 25
 
2.3%
57.4 25
 
2.3%
57.9 24
 
2.2%
57.1 24
 
2.2%
59.4 23
 
2.2%
58.4 22
 
2.1%
59 21
 
2.0%
57.3 21
 
2.0%
58.7 21
 
2.0%
59.9 20
 
1.9%
Other values (95) 785
73.5%
(Missing) 57
 
5.3%
ValueCountFrequency (%)
52.2 1
 
0.1%
52.3 1
 
0.1%
52.9 1
 
0.1%
53.1 3
0.3%
53.2 1
 
0.1%
53.3 2
0.2%
53.4 3
0.3%
53.6 2
0.2%
53.7 1
 
0.1%
53.8 2
0.2%
ValueCountFrequency (%)
64.3 1
0.1%
64 1
0.1%
63.5 1
0.1%
63.4 1
0.1%
63.3 1
0.1%
63.2 1
0.1%
63.1 1
0.1%
63 2
0.2%
62.9 1
0.1%
62.8 2
0.2%

r91_iram1622
Unsupported

Missing  Rejected  Unsupported 

Missing1068
Missing (%)100.0%
Memory size8.5 KiB

pf
Real number (ℝ)

High correlation  Missing 

Distinct247
Distinct (%)23.7%
Missing26
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean2.6753551
Minimum1.36
Maximum6.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-11-08T14:37:58.055169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.36
5-th percentile1.6305
Q11.9
median2.31
Q33.57
95-th percentile3.9395
Maximum6.02
Range4.66
Interquartile range (IQR)1.67

Descriptive statistics

Standard deviation0.87895882
Coefficient of variation (CV)0.32853913
Kurtosis-1.172541
Mean2.6753551
Median Absolute Deviation (MAD)0.62
Skewness0.38943964
Sum2787.72
Variance0.77256861
MonotonicityNot monotonic
2024-11-08T14:37:58.380085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.9 14
 
1.3%
1.93 13
 
1.2%
1.76 13
 
1.2%
2.08 12
 
1.1%
2.03 12
 
1.1%
1.97 12
 
1.1%
1.83 12
 
1.1%
3.82 12
 
1.1%
2.1 12
 
1.1%
1.87 11
 
1.0%
Other values (237) 919
86.0%
(Missing) 26
 
2.4%
ValueCountFrequency (%)
1.36 1
 
0.1%
1.43 1
 
0.1%
1.45 1
 
0.1%
1.46 1
 
0.1%
1.47 1
 
0.1%
1.48 3
0.3%
1.49 3
0.3%
1.51 2
0.2%
1.52 1
 
0.1%
1.53 3
0.3%
ValueCountFrequency (%)
6.02 1
0.1%
5.99 1
0.1%
5.02 1
0.1%
4.8 1
0.1%
4.77 1
0.1%
4.63 1
0.1%
4.49 1
0.1%
4.4 2
0.2%
4.39 1
0.1%
4.26 1
0.1%

so3
Real number (ℝ)

Missing 

Distinct61
Distinct (%)5.8%
Missing22
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean2.8790822
Minimum0.66
Maximum3.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-11-08T14:37:58.764179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.66
5-th percentile2.7525
Q12.84
median2.88
Q32.92
95-th percentile3.02
Maximum3.27
Range2.61
Interquartile range (IQR)0.08

Descriptive statistics

Standard deviation0.13758379
Coefficient of variation (CV)0.047787379
Kurtosis150.401
Mean2.8790822
Median Absolute Deviation (MAD)0.04
Skewness-9.8370511
Sum3011.52
Variance0.0189293
MonotonicityNot monotonic
2024-11-08T14:37:59.050142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.9 96
 
9.0%
2.87 74
 
6.9%
2.86 69
 
6.5%
2.88 67
 
6.3%
2.91 67
 
6.3%
2.89 65
 
6.1%
2.92 58
 
5.4%
2.85 46
 
4.3%
2.84 44
 
4.1%
2.94 40
 
3.7%
Other values (51) 420
39.3%
ValueCountFrequency (%)
0.66 1
 
0.1%
0.79 1
 
0.1%
0.96 1
 
0.1%
2.58 1
 
0.1%
2.6 1
 
0.1%
2.64 1
 
0.1%
2.65 1
 
0.1%
2.66 3
0.3%
2.67 2
0.2%
2.68 1
 
0.1%
ValueCountFrequency (%)
3.27 2
 
0.2%
3.22 1
 
0.1%
3.21 1
 
0.1%
3.2 1
 
0.1%
3.17 1
 
0.1%
3.15 3
0.3%
3.14 2
 
0.2%
3.12 6
0.6%
3.11 1
 
0.1%
3.1 3
0.3%

mgo
Real number (ℝ)

High correlation  Missing 

Distinct61
Distinct (%)5.8%
Missing23
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean0.7590622
Minimum0
Maximum4.32
Zeros6
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-11-08T14:37:59.448602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.55
Q10.66
median0.78
Q30.84
95-th percentile0.93
Maximum4.32
Range4.32
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.18647332
Coefficient of variation (CV)0.24566277
Kurtosis144.85597
Mean0.7590622
Median Absolute Deviation (MAD)0.09
Skewness7.7569225
Sum793.22
Variance0.0347723
MonotonicityNot monotonic
2024-11-08T14:37:59.845678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.84 103
 
9.6%
0.9 47
 
4.4%
0.67 42
 
3.9%
0.79 41
 
3.8%
0.8 41
 
3.8%
0.86 34
 
3.2%
0.82 32
 
3.0%
0.66 29
 
2.7%
0.89 27
 
2.5%
0.77 26
 
2.4%
Other values (51) 623
58.3%
ValueCountFrequency (%)
0 6
 
0.6%
0.26 1
 
0.1%
0.48 1
 
0.1%
0.5 1
 
0.1%
0.51 1
 
0.1%
0.52 5
 
0.5%
0.53 13
1.2%
0.54 19
1.8%
0.55 15
1.4%
0.56 13
1.2%
ValueCountFrequency (%)
4.32 1
 
0.1%
2.93 1
 
0.1%
1.23 1
 
0.1%
1.18 1
 
0.1%
1.13 1
 
0.1%
1.03 2
 
0.2%
1.02 2
 
0.2%
1 3
0.3%
0.99 4
0.4%
0.98 5
0.5%

sio2
Real number (ℝ)

High correlation  Missing 

Distinct324
Distinct (%)31.0%
Missing22
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean20.974713
Minimum17.73
Maximum23.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-11-08T14:38:00.229029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum17.73
5-th percentile19.4225
Q119.83
median21.065
Q321.9975
95-th percentile22.66
Maximum23.04
Range5.31
Interquartile range (IQR)2.1675

Descriptive statistics

Standard deviation1.1617563
Coefficient of variation (CV)0.055388425
Kurtosis-1.4988368
Mean20.974713
Median Absolute Deviation (MAD)1.105
Skewness-0.052784426
Sum21939.55
Variance1.3496778
MonotonicityNot monotonic
2024-11-08T14:38:00.612793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.96 19
 
1.8%
21.86 16
 
1.5%
22.66 15
 
1.4%
21.9 15
 
1.4%
19.83 14
 
1.3%
22.19 14
 
1.3%
21.66 12
 
1.1%
22.1 12
 
1.1%
19.69 11
 
1.0%
21.87 11
 
1.0%
Other values (314) 907
84.9%
(Missing) 22
 
2.1%
ValueCountFrequency (%)
17.73 1
0.1%
18.29 1
0.1%
18.38 1
0.1%
18.63 1
0.1%
18.78 1
0.1%
19.03 1
0.1%
19.05 1
0.1%
19.14 1
0.1%
19.15 2
0.2%
19.16 2
0.2%
ValueCountFrequency (%)
23.04 1
 
0.1%
23.03 2
0.2%
22.94 1
 
0.1%
22.9 2
0.2%
22.89 1
 
0.1%
22.88 1
 
0.1%
22.86 3
0.3%
22.84 1
 
0.1%
22.8 2
0.2%
22.79 4
0.4%

fe2o3
Real number (ℝ)

Missing 

Distinct153
Distinct (%)14.6%
Missing22
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean4.3457361
Minimum0.8
Maximum5.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-11-08T14:38:01.023726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile3.81
Q14.08
median4.31
Q34.65
95-th percentile4.9
Maximum5.29
Range4.49
Interquartile range (IQR)0.57

Descriptive statistics

Standard deviation0.38427684
Coefficient of variation (CV)0.088426179
Kurtosis11.607578
Mean4.3457361
Median Absolute Deviation (MAD)0.29
Skewness-1.2640855
Sum4545.64
Variance0.14766869
MonotonicityNot monotonic
2024-11-08T14:38:01.310772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.29 19
 
1.8%
4.76 19
 
1.8%
4.13 17
 
1.6%
4.77 17
 
1.6%
4.26 16
 
1.5%
4.09 16
 
1.5%
4.23 15
 
1.4%
4.49 15
 
1.4%
4.08 15
 
1.4%
4.69 15
 
1.4%
Other values (143) 882
82.6%
(Missing) 22
 
2.1%
ValueCountFrequency (%)
0.8 1
 
0.1%
0.93 1
 
0.1%
3.3 1
 
0.1%
3.52 1
 
0.1%
3.53 1
 
0.1%
3.59 1
 
0.1%
3.65 1
 
0.1%
3.68 2
0.2%
3.69 3
0.3%
3.7 3
0.3%
ValueCountFrequency (%)
5.29 2
0.2%
5.28 1
0.1%
5.27 2
0.2%
5.26 1
0.1%
5.23 1
0.1%
5.21 2
0.2%
5.2 1
0.1%
5.15 2
0.2%
5.14 2
0.2%
5.13 1
0.1%

caot
Real number (ℝ)

High correlation  Missing 

Distinct329
Distinct (%)31.5%
Missing22
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean62.098948
Minimum59.69
Maximum66.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-11-08T14:38:01.579321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum59.69
5-th percentile60.68
Q161.25
median62.115
Q362.9175
95-th percentile63.55
Maximum66.14
Range6.45
Interquartile range (IQR)1.6675

Descriptive statistics

Standard deviation0.97766103
Coefficient of variation (CV)0.0157436
Kurtosis-0.72433595
Mean62.098948
Median Absolute Deviation (MAD)0.83
Skewness0.029455658
Sum64955.5
Variance0.95582109
MonotonicityNot monotonic
2024-11-08T14:38:01.920819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61.9 17
 
1.6%
61.4 12
 
1.1%
60.9 11
 
1.0%
62.61 11
 
1.0%
62.9 11
 
1.0%
60.87 10
 
0.9%
60.86 9
 
0.8%
62.98 9
 
0.8%
60.98 9
 
0.8%
62.93 8
 
0.7%
Other values (319) 939
87.9%
(Missing) 22
 
2.1%
ValueCountFrequency (%)
59.69 2
0.2%
59.81 1
 
0.1%
59.92 1
 
0.1%
59.93 1
 
0.1%
59.98 1
 
0.1%
60.04 1
 
0.1%
60.08 2
0.2%
60.09 3
0.3%
60.1 1
 
0.1%
60.14 1
 
0.1%
ValueCountFrequency (%)
66.14 1
0.1%
65.51 1
0.1%
64.71 1
0.1%
64.48 1
0.1%
64.3 1
0.1%
63.97 1
0.1%
63.93 1
0.1%
63.91 1
0.1%
63.89 1
0.1%
63.88 2
0.2%

al2o3
Real number (ℝ)

High correlation  Missing 

Distinct200
Distinct (%)19.1%
Missing22
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean4.167782
Minimum2.84
Maximum5.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-11-08T14:38:02.278863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.84
5-th percentile3.4425
Q13.82
median4.04
Q34.65
95-th percentile4.91
Maximum5.43
Range2.59
Interquartile range (IQR)0.83

Descriptive statistics

Standard deviation0.48632914
Coefficient of variation (CV)0.11668776
Kurtosis-0.82640969
Mean4.167782
Median Absolute Deviation (MAD)0.33
Skewness0.13736122
Sum4359.5
Variance0.23651603
MonotonicityNot monotonic
2024-11-08T14:38:02.795845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.9 24
 
2.2%
4.86 23
 
2.2%
4.01 18
 
1.7%
3.89 16
 
1.5%
4.77 16
 
1.5%
4.76 16
 
1.5%
4.02 15
 
1.4%
3.87 15
 
1.4%
3.85 14
 
1.3%
4 14
 
1.3%
Other values (190) 875
81.9%
(Missing) 22
 
2.1%
ValueCountFrequency (%)
2.84 1
0.1%
2.91 1
0.1%
2.92 1
0.1%
2.93 1
0.1%
2.95 1
0.1%
2.99 1
0.1%
3.06 1
0.1%
3.07 1
0.1%
3.08 1
0.1%
3.09 2
0.2%
ValueCountFrequency (%)
5.43 1
 
0.1%
5.31 1
 
0.1%
5.25 1
 
0.1%
5.08 1
 
0.1%
5.06 1
 
0.1%
5.05 2
0.2%
5.04 2
0.2%
5.03 3
0.3%
5.02 3
0.3%
5.01 3
0.3%

na2o
Real number (ℝ)

High correlation  Missing 

Distinct19
Distinct (%)1.8%
Missing25
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean0.085829338
Minimum0
Maximum0.96
Zeros5
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-11-08T14:38:03.097272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03
Q10.04
median0.1
Q30.13
95-th percentile0.14
Maximum0.96
Range0.96
Interquartile range (IQR)0.09

Descriptive statistics

Standard deviation0.05144512
Coefficient of variation (CV)0.59938852
Kurtosis78.261684
Mean0.085829338
Median Absolute Deviation (MAD)0.04
Skewness4.6046222
Sum89.52
Variance0.0026466004
MonotonicityNot monotonic
2024-11-08T14:38:03.407498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.03 234
21.9%
0.1 187
17.5%
0.14 155
14.5%
0.04 119
11.1%
0.13 98
9.2%
0.11 64
 
6.0%
0.09 49
 
4.6%
0.12 45
 
4.2%
0.05 26
 
2.4%
0.15 19
 
1.8%
Other values (9) 47
 
4.4%
(Missing) 25
 
2.3%
ValueCountFrequency (%)
0 5
 
0.5%
0.02 13
 
1.2%
0.03 234
21.9%
0.04 119
11.1%
0.05 26
 
2.4%
0.06 6
 
0.6%
0.07 5
 
0.5%
0.08 14
 
1.3%
0.09 49
 
4.6%
0.1 187
17.5%
ValueCountFrequency (%)
0.96 1
 
0.1%
0.2 1
 
0.1%
0.19 1
 
0.1%
0.16 1
 
0.1%
0.15 19
 
1.8%
0.14 155
14.5%
0.13 98
9.2%
0.12 45
 
4.2%
0.11 64
 
6.0%
0.1 187
17.5%

k2o
Real number (ℝ)

High correlation  Missing 

Distinct85
Distinct (%)8.1%
Missing22
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean0.99210325
Minimum0.03
Maximum9.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-11-08T14:38:03.719033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile0.75
Q10.88
median0.97
Q31.09
95-th percentile1.2
Maximum9.1
Range9.07
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.3934419
Coefficient of variation (CV)0.39657354
Kurtosis345.01999
Mean0.99210325
Median Absolute Deviation (MAD)0.1
Skewness16.90614
Sum1037.74
Variance0.15479653
MonotonicityNot monotonic
2024-11-08T14:38:03.973072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.96 41
 
3.8%
1.2 38
 
3.6%
1.14 36
 
3.4%
0.99 36
 
3.4%
0.9 34
 
3.2%
0.91 33
 
3.1%
0.89 31
 
2.9%
0.93 30
 
2.8%
0.95 29
 
2.7%
1.11 28
 
2.6%
Other values (75) 710
66.5%
ValueCountFrequency (%)
0.03 1
0.1%
0.04 1
0.1%
0.48 1
0.1%
0.51 1
0.1%
0.54 2
0.2%
0.55 1
0.1%
0.56 1
0.1%
0.57 1
0.1%
0.58 1
0.1%
0.6 2
0.2%
ValueCountFrequency (%)
9.1 2
0.2%
2.76 1
 
0.1%
2.69 1
 
0.1%
1.91 1
 
0.1%
1.48 1
 
0.1%
1.37 1
 
0.1%
1.33 2
0.2%
1.32 1
 
0.1%
1.31 1
 
0.1%
1.3 3
0.3%

Interactions

2024-11-08T14:37:37.304775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:27.123860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:31.187573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:34.964957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:39.297004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:47.227722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:52.955152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:57.089755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:01.462089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:05.695103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:09.832060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:13.997253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:17.954197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:21.720515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:25.606332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:29.522067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:33.265494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:37.529432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:27.393859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:31.429512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:35.282919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:39.614197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:47.561992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:53.434312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:57.448749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:01.690600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:05.968894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:10.088051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:14.462828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:18.179944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:21.937824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:25.844318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:29.754878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:33.494588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:37.745966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:27.592443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:31.646738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:35.562361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:40.093137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:47.835552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:53.725864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:57.723000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:01.932273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:06.213533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:10.339105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:14.712761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:18.405766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:22.153770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:26.096006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:29.980130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:33.737779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:37.988740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:27.812974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:31.879270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:35.803750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:40.805466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:48.134217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:53.945158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:57.978782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:02.191501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:06.449310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:10.631712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:14.945436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:18.654520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:22.386867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:26.326979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:30.221035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:33.971394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:38.175843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:28.187267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:32.109730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:36.062016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:41.673635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:48.510580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:54.150940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:58.255165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:02.425647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:06.693868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:10.889204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:15.139416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:18.886903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:22.612180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:26.553818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:30.463065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:34.212596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:38.370269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:28.436639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:32.331300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:36.313715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:42.188497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:48.878761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:54.390410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:58.486458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:02.664153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:06.938459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:11.180794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:15.381226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:19.120838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:22.840955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:26.793371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:30.664652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:34.470388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:38.579188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:28.645099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:32.513493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:36.530328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:43.302937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:49.164145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:54.637275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:58.696400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:03.121466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:07.213303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:11.450201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:15.623357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:19.362678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:22.999123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:27.039066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:30.862190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:34.697831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:38.830244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:28.896875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:32.704084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:36.779425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:43.876297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:49.562588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:54.852637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:58.931499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:03.350227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:07.488271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:11.688122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:15.870424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:19.596040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:23.197042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:27.287734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:31.103815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:35.145803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:39.062793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:29.145708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:32.954360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:37.030198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:44.702790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:49.905882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:55.053139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:59.195888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:03.594646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:07.685303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:11.945203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:16.112485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:19.846948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:23.477988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:27.529825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:31.346170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:35.430217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:39.270956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:29.386194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:33.162273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:37.279140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:45.074517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:50.215497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:55.278486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:59.455494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:03.813804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:07.870668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:12.155849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:16.339310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:20.053909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:23.674245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:27.763482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:31.570759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:35.623684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:39.470953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:29.621329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:33.372594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:37.666302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:45.440034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:50.556119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:55.478049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:59.685042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:04.037948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:08.105406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:12.382562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:16.544861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:20.256768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:23.889074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:27.933077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:31.781423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:35.800055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:39.662016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:29.855333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:33.556620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:37.896283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:45.747495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:51.003527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:55.706769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:59.915962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:04.279861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:08.329639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:12.610087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:16.746448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:20.436196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:24.091089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:28.193314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:31.995514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:36.006334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:39.878715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:30.046181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:33.789728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:38.123882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:46.002719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:51.431165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:55.929710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:00.179435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:04.520567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:08.577136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:12.790318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:16.961963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:20.604946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:24.288592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:28.398240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:32.220775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:36.228502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:40.087554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:30.244749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:34.013919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:38.339360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:46.295392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:51.859557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:56.138466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:00.412510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:04.770476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:08.806834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:13.007738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:17.170446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:20.845760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:24.723790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:28.620767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:32.429722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:36.446060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:40.371161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:30.521396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:34.256102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:38.580706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:46.520696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:52.179590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:56.371152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:00.662381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:04.973874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:09.083970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:13.331028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:17.388229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:21.078931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:24.976726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:28.829609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:32.661994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:36.670578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:40.596515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:30.745872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:34.495643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:38.795327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:46.738955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:52.432661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:56.573227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:00.895984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:05.200670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:09.337828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:13.571099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:17.611560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:21.295642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:25.260197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:29.062444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:32.906607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:36.896208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:40.899760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:30.977902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:34.762286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:39.031477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:46.996693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:52.723431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:36:56.797447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:01.221538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:05.471223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:09.590436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:13.789622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:17.793421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:21.512606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:25.426458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:29.297666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:33.102165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-08T14:37:37.104416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-08T14:38:04.230511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
al2o3caotfe2o3g45µg75µk2omgomlna2opfplr1_iram1622r28_iram1622r2_iram1622r3_iram1622r7_iram1622sbasio2so3
al2o31.000-0.658-0.061-0.1250.0190.5640.2740.0290.717-0.6030.0290.1570.1380.063-0.197-0.1610.2070.769-0.040
caot-0.6581.000-0.2200.1340.046-0.633-0.4840.000-0.6540.6180.0000.0180.1280.0890.2820.323-0.212-0.7170.105
fe2o3-0.061-0.2201.0000.0100.0590.0850.3780.0460.082-0.2810.046-0.213-0.135-0.1060.017-0.0950.1670.2660.069
g45µ-0.1250.1340.0101.0000.520-0.117-0.2160.132-0.1270.2730.132-0.220-0.042-0.122-0.121-0.028-0.093-0.147-0.030
g75µ0.0190.0460.0590.5201.000-0.019-0.1630.046-0.0290.1060.046-0.175-0.075-0.156-0.111-0.140-0.078-0.007-0.054
k2o0.564-0.6330.085-0.117-0.0191.0000.4280.0000.469-0.4400.0000.094-0.117-0.013-0.105-0.2370.1590.567-0.059
mgo0.274-0.4840.378-0.216-0.1630.4281.0000.0000.509-0.4930.000-0.076-0.092-0.0270.078-0.1560.3290.484-0.041
ml0.0290.0000.0460.1320.0460.0000.0001.0000.0000.0740.9980.1840.0470.1351.0000.1880.1030.0920.130
na2o0.717-0.6540.082-0.127-0.0290.4690.5090.0001.000-0.5750.0000.0700.1070.060-0.125-0.1410.3740.697-0.040
pf-0.6030.618-0.2810.2730.106-0.440-0.4930.074-0.5751.0000.074-0.202-0.119-0.1350.0270.124-0.099-0.7500.022
pl0.0290.0000.0460.1320.0460.0000.0000.9980.0000.0741.0000.1840.0470.1351.0000.1880.1030.0920.130
r1_iram16220.1570.018-0.213-0.220-0.1750.094-0.0760.1840.070-0.2020.1841.0000.3200.7130.5100.454-0.0610.133-0.064
r28_iram16220.1380.128-0.135-0.042-0.075-0.117-0.0920.0470.107-0.1190.0470.3201.0000.5130.4870.6400.078-0.0030.109
r2_iram16220.0630.089-0.106-0.122-0.156-0.013-0.0270.1350.060-0.1350.1350.7130.5131.0000.7990.7410.139-0.0130.036
r3_iram1622-0.1970.2820.017-0.121-0.111-0.1050.0781.000-0.1250.0271.0000.5100.4870.7991.0000.6710.190-0.188-0.023
r7_iram1622-0.1610.323-0.095-0.028-0.140-0.237-0.1560.188-0.1410.1240.1880.4540.6400.7410.6711.0000.181-0.2630.090
sba0.207-0.2120.167-0.093-0.0780.1590.3290.1030.374-0.0990.103-0.0610.0780.1390.1900.1811.0000.2130.086
sio20.769-0.7170.266-0.147-0.0070.5670.4840.0920.697-0.7500.0920.133-0.003-0.013-0.188-0.2630.2131.000-0.042
so3-0.0400.1050.069-0.030-0.054-0.059-0.0410.130-0.0400.0220.130-0.0640.1090.036-0.0230.0900.086-0.0421.000

Missing values

2024-11-08T14:37:41.258327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-08T14:37:42.286276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-08T14:37:43.154639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

datericnatplemmlrieg75µg45µsbar1_iram1622r2_iram1622r3_iram1622r7_iram1622r28_iram1622r91_iram1622pfso3mgosio2fe2o3caotal2o3na2ok2o
02018-01-027707STAC3LML37707ST0.344.22396.0NaN31.3NaN50.161.8NaN1.832.890.9922.164.6060.984.860.101.14
12018-01-027702STAC2LML27702ST0.684.45399.019.132.237.949.460.5NaN1.582.880.7122.624.0861.094.580.131.01
22018-01-057722STAC2LML27722ST0.684.45399.0NaN32.0NaN47.461.5NaN1.832.900.9022.764.4961.904.690.121.02
32018-01-077726STAC3LML37726ST0.814.59394.0NaN29.9NaN46.260.1NaN1.972.840.7222.454.0861.754.570.121.02
42018-01-087729STAC2LML27729ST0.383.46392.017.530.135.847.358.5NaN2.012.850.6922.674.1661.074.640.131.04
52018-01-097771STAC3LML37771ST0.423.64411.0NaN30.8NaN45.658.0NaN1.882.910.6922.614.1361.294.640.131.07
62018-01-107725STAC2LML27725ST0.624.75440.0NaN30.5NaN44.055.2NaN2.812.900.6922.404.5562.264.730.121.00
72018-01-127728STAC2LML27728ST0.304.02423.0NaN31.2NaN44.157.3NaN2.212.990.7322.484.0960.744.750.131.32
82018-01-147756STAC2LML27756ST0.343.22411.0NaN30.4NaN46.057.0NaN1.972.910.7122.694.0360.694.270.131.26
92018-01-157761STAC3LML37761ST0.503.77401.0NaN30.2NaN44.957.5NaN1.962.860.6722.444.4860.804.690.141.20
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10592021-04-079825STAC3LML39825ST0.212.12416.017.130.3NaNNaNNaNNaN3.822.840.6920.024.1163.123.810.030.95
10602021-04-089850STAC2LML29850ST0.262.01407.0NaNNaNNaNNaNNaNNaN3.420.664.3220.080.9362.893.860.962.69
10612021-04-089851STAC3LML39851ST0.212.03405.014.7NaNNaNNaNNaNNaN3.552.810.6620.064.4063.043.800.030.90
10622021-04-099856STAC2LML29856ST0.281.79408.0NaN29.7NaNNaNNaNNaN3.572.860.6719.874.2962.803.820.030.93
10632021-04-099857STAC3LML39857ST0.222.15403.0NaN29.6NaNNaNNaNNaN3.732.800.6719.814.3362.733.800.030.93
10642021-04-119866STAC2LML29866ST0.302.12414.0NaN30.4NaNNaNNaNNaN3.562.870.7119.914.1862.943.890.040.99
10652021-04-119867STAC3LML39867ST0.662.95407.015.728.3NaNNaNNaNNaN3.692.860.6819.834.2662.953.830.030.93
10662021-04-139878STAC2LML29878ST0.191.78420.0NaNNaNNaNNaNNaNNaN3.272.920.7119.874.1362.983.880.030.97
10672021-04-139879STAC3LML39879ST0.152.62NaNNaNNaNNaNNaNNaNNaN3.662.900.7019.834.1863.083.850.030.97